Staff AI Systems Engineer

The Opportunity

We’re hiring a Sr. AI Systems Engineer to help support our emerging product, Night Shift, an AI research assistant that amplifies the impact of investigators by automating the tedious, repetitive steps involved in working a case. This role sits within the Machine Learning team and will work closely with partners in Engineering (Backend, Frontend, and Design) in a fast-paced environment. You will be one of the earliest technical contributors to our system architecture for agentic AI, and will own our AI evaluation framework. The outcome we’re after is clear and ambitious: measurably faster, more accurate leads for every officer and every shift.

The Skillset

Familiarity with Agentic Systems: Hands-on experience with LLM agents including:

LLM API use (e.g. LangChain/LangGraph, vLLM, OpenAI/Gemini/Anthropic APIs)

Agent Design: tool use (e.g. via MCP), retrieval, memory, grounding/attribution for claims, and guardrails.

Architectural patterns: planning and hand-off for multi-agent systems, context management

RAG: vector/hybrid search (e.g. pgvector, turbopuffer, rerankers, etc.)

ML Platform expertise: 5+ years building and shipping ML systems to production; experience in the following areas:

Backend Python and JS familiarity required; Typescript/Golang familiarity welcome

Web services (e.g. Express/FastAPI, REST, SSE, JWTs)

Cloud Infrastructure (e.g. AWS, Terraform, VPC, Networking)

Backend databases/stores (e.g. Postgres, Redis)

Observability (e.g. Prometheus, Grafana, OpenTelemetry, LangSmith/Langfuse)

[Preferred] Durable execution (e.g. Temporal, Hatchet)

[Preferred] OLAP (e.g. ClickHouse, Bigquery)

[Preferred] ML Inference (e.g. PyTorch, TensorRT, NVIDIA Triton), ideally in multimodal domains (text/image/video)

[Preferred] Compute orchestration (e.g. Kubernetes, Prefect, Ray)

Experience with LLM Evaluations at scale: You’ve built offline/online eval harnesses and are familiar with the methodologies and metrics to measure:

Search, retrieval, and recommendation performance

Safety & robustness (security, compliance, red-teaming, regression testing)

Cost, performance and latency trade-offs

[Preferred] Agentic task success, trajectory quality, preference learning (e.g. SFT, DPO, RLHF, LLM-as-judge)

Feeling uneasy that you haven’t ticked every box? That’s okay; we’ve felt that way too. Studies have shown women and minorities are less likely to apply unless they meet all qualifications. We encourage you to break the status quo and apply to roles that would make you excited to come to work every day.

90 Days at Flock

We are a results-oriented culture and believe job descriptions are a thing of the past. We prescribe 90 day plans and believe that good days lead to good weeks, which lead to good months. This serves as a preview of the 90 day plan you will receive if you were to be hired in this role at Flock Safety.

The First 30 Days

Immerse yourself in the current system design and agent/tooling landscape. Understand the core customer use cases and data flows.

Support the team by shipping a few quick wins (e.g., refining tool APIs, prompt engineering, fixing bugs)

Stand up the foundational eval and observability scaffolding (datasets, metrics, KPIs, reporting)

Propose a technical architecture and implementation plan for an agent evaluation framework.

The First 60 Days

Deliver the MVP evaluation harness to produce initial metrics, enable debugging and perform regression testing.

Take on a system feature that offers demonstrated improvement against your MVP evaluation suite

90 Days & Beyond

Productionize the evaluation and observability platform and make it the source of truth for quality and safety. (e.g. Online/offline tracing, alerting, dashboards, evaluations and PR-gated regression suite)

Own the roadmap for evolving the agent evaluation platform

Lead deeper R&D threads (e.g., lightweight fine-tuned projection layers, specialized embeddings, multimodal understanding) that can improve system performance on c