01
ML engineer · staff
Location
Americas · ET
Background
ex-Spotify, 9y; recommendation systems at scale; deep PyTorch + JAX; ranked-loss specialist
Stack
Python · PyTorch · JAX · Ray · Spark · BigQuery · Vertex AI
$160/hr
Hire ML Engineers
Senior ML engineers in your team — in 5–10 days.
Pre-vetted ML engineers who've shipped models that move metrics — recommendation, ranking, classification, vision, structured data. From $115/hour, trial sprint included, monthly NTE ceiling so you never get a surprise invoice.
5–10 day staffing · Top 3% pass · No long-term lock-in
Sample profiles
Three ML engineers we'd staff this week.
Anonymized but accurate. Real engineers on our bench, real rates, real availability.
02
ML engineer · senior
Location
EMEA · 6 hr overlap with US ET
Background
ex-DeepMind contractor, 6y; computer vision + structured-data models; production model serving
Stack
Python · PyTorch · ONNX · TorchServe · Triton · Postgres · Snowflake
$140/hr
03
ML engineer · senior
Location
Americas · PT
Background
ex-Stripe Risk, 7y; fraud + risk classifiers; eval rigor; A/B test discipline
Stack
Python · scikit-learn · PyTorch · XGBoost · MLflow · DBT · Airflow
$135/hr
Vetting
Four gates. Roughly 3% pass.
01
Technical screen
Live problem-solving on real ML tasks — feature engineering, model selection, eval design. No generic LeetCode.
02
Modeling deep dive
Candidate walks through a recent production model — architecture, data, metrics, failure modes — with one of our principals.
03
Reference check
Past managers and clients confirm shipped production work, not just titles.
04
Paid trial
Real, scoped work on our team before the engineer faces a customer.
What ML engineers ship for us
Models that move a real metric.
Recommendation systems that lift conversion 8–15%. Fraud classifiers that cut false positives 30–50%. Ranking models that reorder search results to surface what users actually want. Vision models for industrial QA. Forecasting models for inventory and demand.
Every engagement starts with the metric you're trying to move, the eval design that proves you moved it, and the staged rollout that catches regressions before they hit users.
Other engineering roles
Adjacent services
Frequently asked
Common questions.
How much does it cost to hire an AI developer?
Three pricing paths. AISD staff augmentation (senior, AI-native): $95–175/hour depending on seniority and engagement length. Marketplace contract (Toptal, Turing, Upwork): wide range — $40–200/hour with high variance in quality. Full-time hire (US): total comp typically $200k–$450k for a senior AI engineer. The hidden cost in hires is recruiting (3–6 months) and ramp (2–3 months). Staff augmentation pays off when you need impact in <90 days.
What's AISD's vetting process?
Every AISD engineer passes four gates. Technical screen — live problem-solving on AI engineering tasks (not generic LeetCode). System design — they design a production AI system end-to-end with one of our principals. Reference check — past clients confirm shipped production work. Paid trial sprint — a real, scoped piece of work with our team before the engineer faces a customer. Roughly 3% of applicants pass all four.
How is AISD different from a typical software development agency?
Three differences. First, every AISD engineer is senior — minimum 5 years building production software, with shipped AI features. Second, we publish hourly engagement bands and project ranges so you know roughly what an engagement costs before the first call. Third, we take fewer concurrent projects so a partner stays close to delivery.
How does pricing work — fixed-price, T&M, or retainer?
All three. Fixed-price for AI MVPs and agent builds where scope is well-defined after a discovery sprint. Time-and-materials for staff augmentation, billed monthly with a not-to-exceed ceiling. Retainer for ongoing optimization, eval-harness operations, and managed AI services — flat monthly fee for a defined scope of capacity.
How is success measured on an engagement?
Success metrics are defined in writing during scoping and reviewed monthly. Project engagements measure: feature shipped on date, eval-harness pass rate, target business metric (e.g. 'auto-resolve rate ≥35% on customer-support tickets'). Staff augmentation engagements measure: PR throughput, code-review acceptance, and customer-side satisfaction. We do not measure success in hours billed, lines of code, or generic velocity points.
How long does it take to build a production AI agent?
Working prototype: 2 weeks. Production-grade agent (with eval harness, guardrails, observability, and a runbook): 6–10 weeks. The prototype-to-production gap is where most projects fail — the prototype handles the happy path; production has to handle the long tail.
How long does it take to build an AI MVP?
Most AI MVPs at AISD ship a usable version in 4–8 weeks. Week 1 is a discovery sprint. Weeks 2–6 are the build, with weekly demos and a working version by week 4. Weeks 7–8 harden, document, and hand off.
How is AI consulting different from AI development?
Consulting produces decisions and plans; development produces working software. AISD does both, often in sequence: a consulting engagement scopes the architecture and roadmap, then a build engagement implements it. Consulting alone is right when you're early in the AI journey, evaluating vendors, or auditing existing work. Build alone is right when scope is already clear. Most AISD customers do a 2-week paid discovery sprint first — that's a consulting engagement that produces a fixed-price build proposal.